Vector-quantized local features frequently used in bag-of-visual-words approaches are the backbone of popular visual recognition systems due to both their simplicity and their performance. Despite their success, bag-of-words-histograms basically cont
ain low-level image statistics (e.g., number of edges of different orientations). The question remains how much visual information is lost in quantization when mapping visual features to code words? To answer this question, we present an in-depth analysis of the effect of local feature quantization on human recognition performance. Our analysis is based on recovering the visual information by inverting quantized local features and presenting these visualizations with different codebook sizes to human observers. Although feature inversion techniques are around for quite a while, to the best of our knowledge, our technique is the first visualizing especially the effect of feature quantization. Thereby, we are now able to compare single steps in common image classification pipelines to human counterparts.
In this paper, we tackle the problem of visual categorization of dog breeds, which is a surprisingly challenging task due to simultaneously present low interclass distances and high intra-class variances. Our approach combines several techniques well
known in our community but often not utilized for fine-grained recognition: (1) automatic segmentation, (2) efficient part detection, and (3) combination of multiple features. In particular, we demonstrate that a simple head detector embedded in an off-the-shelf recognition pipeline can improve recognition accuracy quite significantly, highlighting the importance of part features for fine-grained recognition tasks. Using our approach, we achieved a 24.59% mean average precision performance on the Stanford dog dataset.
